45 results about "Adaptive coding" patented technology

The invention relates to the technical field of audio and video emergency transmission, and discloses an audio and video low-delay return method and system in an extreme environment. The method comprises the following steps: acquiring original multi-modal data of audio and video acquisition equipment in a target area, and analyzing a data state of the original multi-modal data; and meanwhile, available network transmission links are monitored, and the quality is evaluated. Self-adaptive coding parameters are generated in combination with the link quality and the data state, dynamic coding is executed on original data, and a coding stream suitable for redundant transmission is formed. And carrying out cooperative distribution transmission based on the network link set and the coded stream to generate return data. According to the generation process, a device and energy management instruction is formed and issued to the acquisition and relay device. According to the method, the network state and the content characteristics are optimized in a dynamic coding link in a collaborative manner, and the audio and video quality, the real-time performance and the system energy efficiency which are transmitted back in an extreme environment are improved through a feedback closed loop for transmitting a result to equipment management.

The invention discloses an XBT-T5 observation data deviation analysis and temperature correction method, and relates to the technical field of ocean observation, and the method comprises the steps: obtaining the original temperature observation data of a target sea area based on an XBT-T5 front-end temperature sensor, calculating the depth, generating a temperature-depth profile, and constructing a temperature-depth database in combination with historical depth observation archive data; the method comprises the following steps: synchronously acquiring temperature and depth data by using a CTD temperature-salinity-depth instrument, and carrying out association pairing with a temperature-depth database to generate an XBT-T5 observation data deviation field under the same spatial-temporal scale; based on generation of an XBT-T5 observation data deviation field under the same spatial-temporal scale, designing a dual-channel adaptive encoder, and constructing a temperature-depth collaborative correction model; and according to the temperature-depth collaborative correction model, coupling the temperature-depth database and generating an XBT-T5 observation data deviation field under the same spatial-temporal scale, thereby realizing XBT-T5 observation data deviation analysis and temperature correction. The method improves the quality and application value of observation data.

The invention discloses an ultra-high-definition video stream adaptive coding method based on deep learning visual saliency, and the method comprises the steps: carrying out the five-scale Gaussian filtering processing and image pyramid construction of a video frame, and combining Sobel gradient, Laplacian edge and local binary pattern feature extraction to generate a multi-scale feature map; a pre-training saliency detection network is adopted, and a smooth saliency thermodynamic diagram is generated through processing of a feature adaptation layer, a residual encoder, a self-attention mechanism and a transposed convolution decoder; dividing the video frame into a high region, a middle region and a low region according to the saliency thermodynamic diagram, and establishing a regionalization coding parameter table; performing differentiated prediction modes, motion estimation and quantization strategies on different salient regions; and organizing coded data according to an H.265 / HEVC standard, and embedding the saliency thermodynamic diagram into supplementary enhancement information for transmission. According to the method, the important region concerned by the user can be intelligently identified, a differentiated coding strategy based on content semantics is realized, and the coding efficiency is remarkably improved.

The invention discloses a real-time video processing method and system based on artificial intelligence, and belongs to the technical field of computers. Through deep fusion of multi-source information and a regional adaptive coding mechanism, the problem of collaboration between content generation and network resource allocation in a high-interactivity live broadcast scene is solved. In the feature extraction link, video, audio and text streams are synchronously processed, so that three-dimensional perception of a live broadcast scene is realized; in the multi-mode driving instruction set generation process, audio event features and text interaction features are subjected to semantic association, so that content generation and real-time interaction are highly synchronized, and response delay caused by information splitting in a traditional scheme is avoided; according to the regional differentiation coding processing, resource allocation is dynamically adjusted according to the regional importance of visual semantic feature recognition and the network state, and the coding quality of a digital human region is preferentially guaranteed, so that the stability of core visual elements is maintained under the network fluctuation condition, and the intelligent level of real-time video processing and the user experience are improved.

The invention relates to an underwater image enhancement method based on a regional differentiation fusion network, and belongs to the technical field of image information processing. The method comprises the following steps: inputting an original underwater image, and executing self-adaptive histogram equalization processing for limiting contrast to generate a corresponding self-adaptive enhanced image; respectively inputting the original underwater image and the self-adaptive enhanced image into an original encoder and a self-adaptive encoder, and generating initial original features and self-adaptive enhanced features through a shallow feature extraction module; the original features and the self-adaptive enhancement features are input into a cross fusion Swin Transform module, and regional differentiation feature fusion is carried out; inputting the fused regional differentiation features into a reconstruction decoder, carrying out gradual decoding and reconstruction through interpolation up-sampling and convolution activation operation, and outputting an enhanced underwater image; in the training stage, a joint loss function is adopted to supervise and optimize an enhancement result, and the joint loss function comprises Charbonier loss and VGG loss.

The invention provides a dual-mode while-drilling azimuth resistivity measurement method and device and related equipment, and relates to the technical field of charging management. According to the technical scheme provided by the invention, the gradient mutation event in the resistivity curve is identified through the gradient feature, and the key geological interface and the abnormal region are accurately positioned. The data transmission mode is determined by collecting the drilling working condition parameters corresponding to the gradient sudden change event, so that the transmission strategy is matched with the actual drilling state. And the transmission priority score of each data is calculated based on the drilling working condition parameters, the gradient sudden change event and the resistivity curve, so that quantitative evaluation of data importance and timeliness is realized. And comparing the transmission priority score with a preset percentage threshold, and then distinguishing first data and second data to ensure that important data is preferentially transmitted and secondary data is delayed to be stored. The first data are transmitted after being adaptively coded, and the second data are stored in a layered manner, so that the requirements of real-time performance and integrity are considered under the limited transmission bandwidth.

This invention provides a lightweight governance and coordinated response method for urban rail transit video, comprising: collecting urban rail transit video streams and corresponding business data; establishing a unified spatiotemporal benchmark and data mapping through preprocessing; extracting static background images through background modeling; decoupling the video in real time into static background and dynamic foreground; and constructing a ring-shaped buffer at the edge; extracting structured semantic features from the dynamic foreground to form a lightweight video stream for transmission and storage; integrating business data and feature analysis; triggering a first-level wake-up command when the warning rules are met; extracting the original foreground pixel stream T seconds before the warning from the buffer; generating key event frames using adaptive ROI encoding; matching and recording coordinated response rules based on the warning results; and finally saving the lightweight video stream, key event frames, warning results, and response records.

This invention discloses a 3D seismic fault segmentation method based on 3D adaptive coding and cross-domain collaborative learning, which addresses the problems of scarce annotations, the inability of 2D models to effectively represent 3D structures, and unstable predictions across different work areas. The method first preprocesses the source domain labeled and the target domain unlabeled seismic data. A lightweight 3D adapter is inserted based on the frozen backbone parameters of the 2D visual model, and a dual-path encoder with spatial and depth perspectives is constructed, fusing features from both perspectives to form a joint 3D representation. Then, a lightweight 3D decoder is used to progressively restore resolution and generate a fault probability volume. The source domain uses binary cross-entropy and Dice loss for supervised learning; the target domain uses a heterogeneous bi-sub-network composed of a visual branch and a 3D convolutional branch. Pseudo-labels are adaptively generated through voxel confidence, and unsupervised training is achieved by combining consistency and difference regularization, thus enabling stable and reliable 3D fault segmentation across different work areas even without target domain labels.

The application discloses a full-media full-signal integrated management platform, and belongs to the technical field of information processing and communication. The application comprises: a timing perception intelligent core, a network transmission control module, and a full-media signal processing module. The timing perception intelligent core comprises a global clock synchronization module, a delay prediction compensation module, an intelligent signal routing module, and an adaptive coding and decoding engine. The network transmission control module comprises a network adaptive transmission engine, a multi-path load balancer, and a fault switching recovery module. The full-media signal processing module is constructed based on a micro-service architecture and comprises a multi-modal signal acquisition module, a signal preprocessing module, a media content management center, a timing synchronization processing tool, and a unified API gateway. The intelligent resource scheduling pool provides intelligent scheduling management of computing, storage, network, and AI computing power, and realizes resource demand prediction, multi-target optimization distribution, and automatic elastic expansion and contraction.

The invention relates to the technical field of emergency communication and audio and video transmission, and discloses an audio and video emergency return method and system based on cooperation of multi-source heterogeneous equipment. The method includes receiving a set of raw perceptual data from a plurality of independent devices, including uncompressed audio and video streams, sensor readings, and network probe data packets. And performing multi-link survivability evaluation according to the network detection data packet, constructing a transmission channel in real time, and generating a multi-modal network convergence transmission instruction. Based on the instruction and an original audio and video stream, content-aware analysis is performed to dynamically derive coding parameters, and an adaptive coding control signal is generated. And generating a multi-device collaborative management instruction for coordinating a device protocol stack and resource allocation in combination with the device state report and the network situation. And integrating the instructions and the information to form a return execution decision, and scheduling multiple devices to cooperatively complete self-adaptive return of the audio and video data. According to the invention, the link robustness and the content transmission efficiency of audio and video return in the emergency scene are improved.

In various examples, properties may be determined for image regions, where the image regions are indicated by output data generated using MLMs. An encoder may use the properties to generate encoded images using encoding quality settings for the image regions. When an encoded image is decoded and applied to the MLMs, corresponding output data may indicate an image region which is likely to correspond to an encoded image region of the encoded image, and which may be applied to at least one MLM. Thus, the properties for encoding an image region to an encoded image can be adapted to control the visual quality of an image region determined from a decoded version of the encoded image. The properties may be determined based at least on performance metric values for the MLMs or based at least on a ranking of the image regions.

This invention discloses a multi-terminal network audio broadcasting system, belonging to the field of audio broadcasting technology. It includes a central server and multiple distributed audio terminals. The central server comprises: a geographic information module for storing digital elevation models and the deployment locations of each audio terminal; an adaptive coding module for dynamically adjusting the redundancy coding rate of the audio stream based on link quality feedback reported by each audio terminal; and a routing calculation module for generating a multi-hop broadcast tree based on a terrain-compensated propagation loss model. Each audio terminal includes a positioning module for collecting its real-time geographic coordinates and reporting them to the central server. This invention achieves accurate path loss calculation, adaptive redundancy coding, dynamic power adjustment, intelligent broadcast tree construction, efficient packet loss recovery, and dynamic relay coverage, improving the coverage, stability, and energy efficiency of audio broadcasting.

The invention specifically relates to an unmanned cluster identification method based on motion topology reasoning, and belongs to the technical field of artificial intelligence and unmanned systems. The method comprises the following steps: firstly, collecting a historical track sequence of unmanned cluster nodes, and constructing an interpretable feature system containing three types of physical features of motion, interaction and topology; inputting the feature vector into a self-adaptive encoder containing dynamic graph construction, multi-head attention and time sequence aggregation components, and outputting a high-order cluster feature vector of time sequence aggregation; then, target identity recognition and node interaction topology reasoning are completed in sequence, and a cluster node future trajectory prediction result is output through a dual decoder; and finally, identifying and predicting performance through joint optimization of a multi-objective loss function, and updating model parameters in real time to adapt to a dynamic task scene. According to the method, the problems of poor scale adaptability, task decoupling and feature black box of an existing method are effectively solved, and end-to-end joint optimization of identity recognition and trajectory prediction is realized.

The invention relates to an adaptive coding compression method and system for a column database, belongs to the technical field of data compression, and solves the problems of low compression rate and low query performance caused by curing of a compression algorithm and strong manual dependence in the prior art. Comprising the following steps: partitioning collected original data, and extracting a multi-dimensional feature vector from each dynamic column in each data block; inputting the multi-dimensional feature vector of each dynamic column into a trained prediction model to obtain a prediction result, the prediction result comprising a compression algorithm selection probability, a compression ratio prediction value and a decompression overhead level; based on the prediction result of each dynamic column, selecting an optimal compression algorithm for the dynamic columns through a multi-objective decision-making method; and further executing a compression operation. And the compression speed and precision of mass data are improved.

The application relates to the technical field of audio and video emergency transmission, and discloses an audio and video low-delay back transmission method and system in an extreme environment. The method comprises the following steps: acquiring original multi-modal data of an audio and video acquisition device in a target area, and analyzing the data state; simultaneously monitoring available network transmission links, and evaluating the quality thereof. Adaptive coding parameters are generated in combination with the link quality and the data state, dynamic coding is performed on the original data, and coding streams suitable for redundant transmission are formed. Based on the network link set and the coding streams, collaborative distribution transmission is carried out, and back transmission data is generated. According to the generation process, device and energy management instructions are formed and issued to the acquisition and relay devices. In the dynamic coding link, the network state and the content characteristics are collaboratively optimized, and through the feedback loop of the transmission result to the device management, the audio and video quality, the real-time performance and the system energy efficiency of the back transmission in the extreme environment are improved.

The invention discloses a high-throughput satellite communication system control surface pressure test system and method, and relates to the technical field of satellite communication. The invention discloses a high-throughput satellite communication system control plane pressure test system and method for solving the problems that a high-throughput satellite communication system is huge, the number of far-end stations is large, the data size of a control plane is large, the process is complex, and system networking scale test verification cannot be effectively carried out conveniently. A set of complete semi-physical pressure test environment and test method is established by constructing a simulation channel environment and simulating a control plane interaction process and protocol between gateway station channel equipment and a far-end station; according to the invention, the testing of the user access quantity, the high-capacity service request, the adaptive code modulation (ACM) capability and the like of the high-throughput satellite communication system under the control face is realized. The method can quickly verify the capability index of the whole satellite communication system, and has the advantages of hybrid networking with a real remote station, vivid channel environment, simple configuration, easy deployment, high efficiency, low cost and the like.

The invention relates to a compressed sensing-based remote sensing image full-process adaptive coding and decoding method, and relates to the technical field of space remote sensing imaging. The method comprises the following steps of: calculating an image content complexity self-adaptive selection coding mode by utilizing a maximum between-class difference method; extracting an image feature factor, establishing a significance model of a corresponding mode, and guiding the coding tree block to carry out adaptive form partitioning; detecting the sparsity of the coding block through an information density function to mine the sparsity of the image, and realizing the adaptive sampling rate distribution of the image; and adaptively selecting a reconstruction matrix and a sparse base according to the category of the coding block, setting an iteration threshold according to the known sparseness, and reconstructing the coding block by using an orthogonal matching pursuit algorithm. According to the method provided by the invention, the compression ratio does not need to be manually preset, the sampling mode can be selected according to the image content, the compression efficiency is adaptively set by detecting the sparseness, and the requirements of on-satellite rapid compression and ground high-fidelity reconstruction are met.

A recommendation method and system based on meta data augmentation relates to the field of personalized recommendation technologies. The method includes: training a cross-domain adaptive encoder-decoder model through user preference data; meta-augmentation ratings of a target domain user-item pair through a trained cross-domain adaptive encoder-decoder model; performing meta-learning training on a recommendation model; and recommending items to a user through a trained recommendation model. In the present disclosure, prior to the meta-learning training of the recommendation model, the cross-domain adaptive encoder-decoder model is trained through the user preference data, and data required for the meta-learning training of the recommendation model is meta-augmentation through the cross-domain adaptive encoder-decoder model, thereby effectively resolving a meta-overfitting problem in the existing meta-learning training of the recommendation model caused by the data sparsity and cold-start problem, and accurately recommending a user's preferred items to the user.

The invention relates to the technical field of risk management and control, and provides a risk pre-management and control method based on data integration and operation risk source governance, and the method comprises the steps: obtaining an insurance cancellation application text, an insurance policy amount and operation time sequence data, carrying out the semantic decoupling through employing a dual-branch encoder, and outputting universal and professional semantic features, and performing multi-modal fusion with standardized data, processing the data by a progressive alignment module and an adaptive encoder, inputting the processed data into a double-layer memory architecture, outputting a risk score and a management and control instruction through a hierarchical attention mechanism, executing operation blocking, feeding back and updating a risk event library, and optimizing model parameters by adopting an online learning algorithm. According to the invention, the pre-recognition and intelligent management and control of the operation risk are realized, and the accuracy and real-time performance of insurance service operation risk management are improved.

The invention particularly relates to an Internet of Things floating point time sequence data compression transmission and decompression reading method based on a GPU. According to the scheme, an asynchronous pipeline scheduling mechanism driven by a lightweight asynchronous event is adopted in the data batch transmission and compressed data flow return process between a CPU and a GPU, so that the problem of high transmission delay caused by data transmission in an existing GPU compression scheme is solved; on the other hand, an accurate and rapid floating-point number-to-integer conversion method with theoretical guarantee is provided, and the problem of precision loss existing in a traditional rapid conversion method is solved; besides, a bit-plane adaptive coding scheme is also designed, so that an optimal storage strategy can be dynamically selected for different parts of the data, and the negative influence of an abnormal value on the compression ratio is effectively overcome. According to the scheme provided by the invention, on the premise of ensuring that the data is absolutely lossless, the compression and decompression speed obviously superior to those of the existing CPU and GPU schemes can be realized, and the data communication transmission compression ratio and throughput in the Internet of Things system can be obviously improved.

Existing steganography techniques generate pseudo-sequences that retain only a small portion of the statistical distribution features of the original DNA sequence, resulting in poor concealment and weak resistance to steganalysis, thus leading to low security. This invention proposes a sequence-generating DNA steganography method based on conditional probability adaptive coding. First, an RNN network is used to learn the statistical distribution features of natural DNA. Then, an adaptive grouping algorithm generates pseudo-sequences highly similar to the original DNA. Experimental results show that this method significantly improves the concealment and resistance to steganalysis of the pseudo-sequences, enhancing the security of information hiding. Furthermore, given the lack of a comprehensive evaluation mechanism for the concealment of pseudo-sequences generated by existing DNA steganography techniques, this invention proposes using three parameters to evaluate the perceptual and statistical concealment of the pseudo-sequences, further refining the evaluation system for the security of DNA steganography systems.

The invention discloses a fundus image retinal vessel segmentation method, device and system, and a storage medium, and the method comprises the steps: S1, obtaining to-be-segmented fundus image data, and carrying out the preprocessing; s2, constructing a segmentation model based on receptive field attention and cross attention decoding according to the preprocessed fundus image data; performing end-to-end feature extraction and pixel-level classification on the image by using the segmentation model; wherein the segmentation model comprises an RFAConv adaptive encoder, a self-attention bottleneck module, a feature enhancement jump connection module and a decoding module based on cross attention. According to the technical scheme, the defects of the prior art in the aspects of feature extraction flexibility and feature fusion effectiveness are overcome, and the edge precision and robustness of fundus image retinal blood vessel segmentation are improved.

The invention discloses a hydropower long sequence retrieval method, system and device based on a time sequence embedded large model and a medium, and belongs to the technical field of artificial intelligence and hydraulic engineering crossing. The method comprises the steps that multivariable long time sequence data are input, and the multivariable long time sequence data are time sequence records of hydropower operation parameters; performing time sequence adaptive coding on the multivariable long time sequence data through a core processing layer; outputting a unified semantic embedding space to support cross-modal retrieval of the text and the time series data; and processing user query through a multi-modal retrieval mechanism, wherein the multi-modal retrieval mechanism performs semantic matching based on a preset engineering query template. According to the method, through an adaptive frequency modulation mechanism, a frequency modulation coefficient is calculated through a modulation function according to multivariable long time sequence data, and a basic frequency parameter of rotary position coding is dynamically adjusted, so that the model can be adaptive to periodic change characteristics of hydroelectric data.

A dynamic element matching (DEM) coding system configured to convert an N-bit control word into a pattern of 1-bit values. The DEM encoder system comprises a plurality of bypassable switching blocks, including an encoder input configured to receive the N-bit control word, and a plurality of control outputs configured to provide a plurality of intermediate control values ​​based on the N-bit control word, the plurality of bypassable switching blocks being connected in series, and a plurality of DEM encoders configured to receive the plurality of intermediate control values ​​and generate a plurality of encoder output values ​​based on the plurality of intermediate control values, each encoder output value being a corresponding 1-bit value of the pattern of 1-bit values.

The invention provides a complexity-aware end-to-end coding and decoding complexity control method and system, and the method comprises the steps: determining the complexity candidate set of each module to represent the selectable channel number configuration, and enhancing the flexible adjustment capability of the model complexity; secondly, designing a complexity sensing module, and combining with complexity constraint adjustment to generate dynamic configuration of a model network; and finally, a multi-stage network model training scheme is adopted, and the difference between the target complexity and the current complexity is incorporated into model training constraints in the loss function design process, so that effective training of the network can be realized. According to the invention, complexity control of end-to-end depth video coding is effectively solved, and adaptive coding and decoding with different complexity application requirements are realized.

The invention relates to the technical field of detection and analysis, and discloses an online counting and classifying method for micron-sized particles in a concentrated solution. The method comprises the following steps: collecting a dynamic image sequence of a concentrated solution through a high-speed microscopic imaging unit and synchronously obtaining optical and fluid parameters; constructing a physical optical forward model based on the Mie scattering theory to perform background correction and scattering enhancement; after particle multi-dimensional invariant features are extracted, unsupervised domain adaptive encoders are adopted to decouple content and style features, and cross-domain knowledge migration is realized through adversarial training; and combining space-time correlation tracking and track uniqueness counting, and outputting the number concentration of each type of particles in unit volume. According to the method, high-precision and cross-production-line online particle monitoring can be realized without target domain labeling, and the detection robustness and the automation level are remarkably improved.

The invention discloses a 360-degree video saliency prediction method and system based on spherical surface time-space-frequency domain feature fusion. According to the method, an improved spherical ConvLSTM (Long Short-Term Memory) network is combined with a multi-scale feature fusion mechanism, and combined modeling is carried out on the space structure, the motion dynamic state and the frequency domain energy distribution of a video. Through a multi-stage encoder-decoder structure, accurate capture of motion rhythm and structure change in a dynamic scene is realized, and a saliency map conforming to a human vision law is generated. The system shows better prediction precision and global consistency than a mainstream model in various 360-degree video scenes, and can be applied to the fields of virtual reality video transmission, view field prediction, content adaptive coding and the like.

The embodiment of the invention discloses a processing system and method based on multi-terminal heterogeneous data. The system comprises a self-adaptive coding and decoding sub-module, a distributed current-limiting collaborative sub-module and a global collaborative scheduling module, wherein the self-adaptive coding and decoding sub-module selects an optimal coding and decoding algorithm based on multi-dimensional features of the multi-terminal heterogeneous data; the distributed current-limiting cooperation sub-module dynamically distributes a current-limiting threshold value of a distributed node based on the decoded data volume, the processing time consumption and the data priority; and the global collaborative scheduling module adjusts the coding and decoding parameters of the adaptive coding and decoding sub-module and the current limiting threshold of the distributed current limiting collaborative sub-module in real time. According to the invention, by constructing a dynamic cooperation mechanism of coding, decoding and current limiting, the problems of poor multi-terminal data adaptability, resource waste and current limiting misjudgment are solved, and the efficiency, the stability and the resource utilization rate of a data acquisition link are improved.

This invention relates to the field of emergency communication and audio / video transmission technology, and discloses a method and system for collaborative audio / video emergency backhaul using multi-source heterogeneous devices. The method includes receiving raw sensor data sets from multiple independent devices, including uncompressed audio / video streams, sensor readings, and network probe data packets. Based on the network probe data packets, multi-link survivability assessment is performed, and a transmission channel is constructed in real time, generating a multi-modal network fusion transmission command. Based on this command and the raw audio / video stream, content-aware analysis is performed to dynamically derive coding parameters, generating an adaptive coding control signal. Combining device status reports and network situational awareness, a multi-device collaborative management command is generated to coordinate device protocol stacks and resource allocation. The above commands and information are integrated to form a backhaul execution decision, scheduling multiple devices to collaboratively complete the adaptive backhaul of audio / video data. This invention improves the link robustness and content transmission efficiency of audio / video backhaul in emergency scenarios.

This application discloses a data transmission method, apparatus, electronic device, and computer-readable storage medium. The embodiment determines at least two data transmission links corresponding to the data stream to be transmitted at the current time by the sending end. Then, it obtains the network quality detection result of each data transmission link and, based on the network quality detection result, selects at least one primary transmission link and at least one secondary transmission link from the data transmission links. Next, based on the network quality detection result corresponding to the primary transmission link and the data packet to be transmitted, at least one redundant data packet is generated. Finally, the data packet to be transmitted is transmitted to the receiving end according to the primary transmission link, and the redundant data packet is transmitted to the receiving end according to the secondary transmission link. This scheme can adaptively encode and transmit according to network conditions, effectively saving the amount of network transmission data in redundant transmission schemes while ensuring data transmission reliability.